The latest from the Data Assimilation Research Testbed: New algorithms for non-gaussian distributions and sampling errors; new memory management for larger models and faster execution; new model and o

Raeder, Kevin D.; Anderson, Jeffrey L.; Gharamti, Mohamad; Kershaw, Helen; Raczka, Brett; Johnson, Benjamin K.; Smith, Marlena; Liu, Ed; Labriola, Jon; Ishraque, Fairuz; Hagan, Daniel

Raeder, K. D., Anderson, J. L., Gharamti, M., Kershaw, H., Raczka, B., … Johnson, B. K. (2022). The latest from the Data Assimilation Research Testbed: New algorithms for non-gaussian distributions and sampling errors; new memory management for larger models and faster execution; new model and o. In AGU Fall Meeting 2022. American Geological Union (AGU): Chicago, IL, US.

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Author(s)

Kevin D. Raeder-NCAR/UCAR

Jeffrey L. Anderson-NCAR/UCAR

Mohamad Gharamti-NCAR/UCAR

Helen Kershaw-NCAR/UCAR

Brett Raczka-NCAR/UCAR

Benjamin K. Johnson-NCAR/UCAR

Marlena Smith-NCAR/UCAR

Ed Liu

Jon Labriola

Fairuz Ishraque

Daniel Hagan

Title

The latest from the Data Assimilation Research Testbed: New algorithms for non-gaussian distributions and sampling errors; new memory management for larger models and faster execution; new model and o

Date

2022-12-14

Resource Type

conference material

Conference

AGU Fall Meeting 2022

Meeting Sponsor

American Geological Union (AGU)

Peer Review

Non-refereed

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OpenSky Citable URL

http://n2t.net/ark:/85065/d78919xx

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The latest from the Data Assimilation Research Testbed: New algorithms for non-gaussian distributions and sampling errors; new memory management for larger models and faster execution; new model and o

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